The prior over network weights corresponds to a variational dropout prior [1]}. The encoding distribution is parametrized by a set of independent normal distributions over network weights. We adopted the approximation suggested by [2]} to estimate the KL divergence between the encoding and prior distribution and obtained gradients with respect to \(\mathbf {\Lambda }\)  using the reparametrization trick [3]}.
